PELAGIX：ブルーカーボン資産の検証済み定量化と透明な取引のためのインテリジェント分散型フレームワーク
PELAGIX: An Intelligent Decentralized Framework for Verified Quantification and Transparent Trading of Blue Carbon Assets (原題)
Sanket Jaiswa, Satish Patel, Soham Nimje, Prachi Jain, Spandan Karandikar, Srujan Barshe
🤖 gxceed AI 要約
日本語
Sentinel-2とGEDIデータを用い、マングローブの地上部炭素を推定する統合ワークフローを提示。NDVIマスクと経験的係数でAGCを算出し、GEEとInVESTの結果を相補的だが非等価なモデル出力と位置づける。パラメータ感度分析とデジタルMRV来歴層のプロトタイプを示し、炭素クレジット認証ではなく証拠管理の枠組みであると明示する。
English
This study presents an integrated workflow for mangrove mapping and above-ground carbon estimation using Sentinel-2 and GEDI data in the Sundarbans, with a prototype digital MRV provenance layer. It reports modeled AGC of ~443 kt C, contrasts GEE and InVEST outputs as complementary but non-equivalent, and quantifies parameter sensitivity. The authors stress this is evidence handling, not carbon-credit certification.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
ブルーカーボンはJ-クレジットやブルーカーボン生態系の保全に絡み、MRVの透明性・来歴管理は国内炭素会計インフラ整備の参考になる。SSBJ開示における自然資本・生物多様性項目とも接続しうる。
In the global GX context
Digital MRV provenance for nature-based carbon is central to ISSB/CSRD disclosure and integrity debates in voluntary carbon markets. The paper's explicit separation of modeled estimates from certification speaks directly to global MRV trust and greenwashing concerns.
👥 読者別の含意
🔬研究者:衛星リモートセンシングと経験的係数を用いた炭素推定の不確実性とモデル間比較の扱いを学べる。
🏢実務担当者:ブルーカーボン・自然資本プロジェクトのMRV来歴管理とデータ透明性設計の参考になる。
🏛政策担当者:炭素クレジット認証とモデル推定の区別を制度設計にどう反映するかの示唆を得られる。
📄 Abstract（原文）
Mangrove carbon assessments require spatially explicit methods whose assumptions, data support, and verification status remain traceable. This study presents an integrated workflow for operational mangrove mapping, modeled above-ground carbon (AGC) estimation, complementary carbon accounting, parameter sensitivity, and a prototype digital measurement, reporting, and verification (MRV) provenance layer for a Sundarbans area bounded by approximately 88.10–89.10°E and 21.50–22.35°N. Sentinel-2 surface-reflectance observations from 1 January to 30 April 2023 were cloud-screened, median composited, and used to calculate NDVI; NDVI > 0.4 defined the operational mangrove mask. The supplied Google Earth Engine workflow quality-screened GEDI monthly rh100, median-composited it, applied its original rh100/100 transformation, and filled unobserved structural values using 15× NDVI. Biomass and carbon were modeled as AGB = H× NDVI× 10.5 and AGC = 0.47× AGB. The retained raster footprint contains 15,793,243 mangrove cells (157,932.43 ha under the nominal 0.01 haper-cell convention). Mean AGC density was 2.8060 t C ha−1 (median 2.7935; standard deviation 0.6325), with total modeled AGC of 443,164.99 t C and derived AGB of 942,904.24 t. A separate InVEST baseline reports 409,383 t C. Combined variation of the project-specific AGB coefficient and carbon fraction produced 343,486.54–550,027.47 t C (-22.49% to +24.11%); this is parameter sensitivity, not a confidence interval. GEE and InVEST outputs are complementary, non-equivalent model results. The prototype demonstrates evidence handling and digital provenance workflows, not carbon-credit certification or independent verification. No independent field validation was available, and reported values represent modeled above-ground carbon rather than total ecosystem carbon.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.22214/ijraset.2026.85316first seen 2026-10-29 04:46:58 · last seen 2026-11-06 04:46:43
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